AI-900 Describe features of generative AI workloads on Azure Practice Test 3 — 15 Questions

Question 1

A social media company uses Azure OpenAI Service to automatically generate captions for user-uploaded images. The company has a strict content policy that prohibits any generated captions containing profanity, hate speech, or self-harm references. Which feature of the Azure OpenAI Service should the company configure to automatically block such harmful content?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Content filtering. Content filtering is the correct feature because it is specifically designed to detect and block harmful content such as profanity, hate speech, and self-harm references in both input prompts and generated outputs. Azure OpenAI Service's content filtering system uses multi-class classification models to enforce responsible AI policies automatically, without requiring custom training or manual moderation.

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Temperature parameter

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Top-p parameter

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Max-tokens parameter

Question 2

A developer uses Azure OpenAI Service to generate marketing copy. They want the model to produce more focused and deterministic responses, reducing the variety of outputs for the same prompt. Which parameter should the developer decrease?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Lowering temperature (e.g., from 1.0 to 0.2) makes the model more deterministic and focused, reducing output variety for the same prompt. This is the correct parameter to adjust for more consistent marketing copy.

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Max tokens

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Top P

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Frequency penalty

Question 3

A legal research firm uses Azure OpenAI Service to answer questions about specific case law documents. They want the model to base its answers exclusively on the content of the provided documents, without using any external knowledge from its training. Which approach should they use?

Accepted Answer

Use the 'Azure OpenAI on your data' feature with a 'Search' data source containing the documents. The 'Azure OpenAI on your data' feature with a 'Search' data source allows the model to retrieve and ground its answers exclusively on the content of the provided documents. This approach uses a search index (e.g., Azure Cognitive Search) to fetch relevant document chunks and inject them into the prompt, ensuring the model does not rely on its pre-trained knowledge. It is the only method that enforces strict document-based grounding without external knowledge leakage.

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Increase the 'temperature' parameter to 0.0

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Use the system message to instruct the model to only use provided documents

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Set the 'max_tokens' parameter to a low value

Question 4

A developer uses Azure OpenAI Service to generate short product descriptions. The developer notices that the model sometimes produces nonsensical or very low-probability words that make the output less coherent. The developer wants to reduce the chance of such outputs while still allowing some creative variability. Which parameter should the developer adjust in the API request?

Accepted Answer

Set the top_p parameter to a value like 0.9. Setting `top_p` to 0.9 (nucleus sampling) instructs the model to consider only the tokens whose cumulative probability mass reaches 90%, thereby cutting off very low-probability (nonsensical) tokens while still allowing creative variability from the top 90% of likely tokens. This directly addresses the developer's goal of reducing incoherent outputs without fully deterministic generation.

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Decrease the temperature parameter to 0.1

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Increase the stop parameter to include more stop sequences

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Increase the max_tokens parameter to allow longer descriptions

Question 5

A social media platform uses Azure OpenAI Service to generate summaries of user comments. The development team discovers that sometimes the generated summaries include offensive or harmful language that was present in the original comments. The team wants to ensure that the generated output is always free of hate speech, profanity, and self-harm references. What should the team configure in the Azure OpenAI Service?

Accepted Answer

Configure a content filter. Azure OpenAI Service provides built-in content filtering that can be configured to block hate speech, profanity, and self-harm references in both input prompts and generated completions. This ensures that even if offensive language appears in the original user comments, the generated summaries will be free of such harmful content. The content filter operates at the service level, applying predefined severity thresholds to filter out undesirable language.

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Set the temperature parameter to 0

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Increase the max_tokens parameter

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Use a grounding source

Question 6

A marketing team wants to create original images for advertisements based on text descriptions. Which Azure OpenAI Service model capability should they use?

Accepted Answer

DALL-E. DALL-E is the correct choice because it is the Azure OpenAI Service model specifically designed for generating original images from natural language text descriptions. Unlike other models in the suite, DALL-E uses a diffusion-based architecture to create photorealistic or stylized visuals based on prompt inputs, making it ideal for the marketing team's goal of producing custom advertisement imagery.

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GPT-3.5

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Codex

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Azure Speech-to-Text

Question 7

A developer uses Azure OpenAI Service to generate long-form articles. The developer notices that the model tends to repeat the same sentence structures and vocabulary, making the output monotonous. Which parameter should the developer increase to reduce this repetition?

Accepted Answer

C. Increasing the 'frequency penalty' parameter (option C) reduces repetition by penalizing tokens that have already appeared in the generated text. This encourages the model to use a wider variety of sentence structures and vocabulary, making the output less monotonous.

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A

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B

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D

Question 8

A developer is using Azure OpenAI Service to classify customer support tickets into categories such as 'Billing', 'Technical Issue', and 'Account Management'. The developer provides three labeled examples for each category in the prompt to improve the model's accuracy. What technique is the developer applying?

Accepted Answer

Few-shot learning. Few-shot learning is the correct technique because the developer is providing a small number of labeled examples (three per category) directly in the prompt to guide the model's output without updating the model's weights. This approach leverages the model's in-context learning ability, where the examples act as a pattern for the model to follow when classifying new tickets.

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Fine-tuning

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Prompt engineering

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Retrieval-augmented generation

Question 9

A meeting transcription service needs to convert multilingual audio recordings into accurate text in real time. Which Azure OpenAI Service model is specifically designed for this task?

Accepted Answer

Whisper. Whisper is the Azure OpenAI Service model specifically designed for speech-to-text transcription, including multilingual audio recordings, and it supports real-time conversion. Unlike GPT-4, which is a large language model for text generation, Whisper is optimized for audio processing tasks such as transcription and translation. This makes it the correct choice for converting multilingual audio into accurate text in real time.

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GPT-4

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DALL-E 2

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Codex

Question 10

A company uses Azure OpenAI Service to generate product descriptions for an e-commerce site. They want to ensure that the generated descriptions never contain offensive, violent, or hateful content. Which built-in feature should the developer enable in the Azure OpenAI Service?

Accepted Answer

Content Filtering. Content Filtering is a built-in safety feature in Azure OpenAI Service that automatically detects and blocks harmful content categories such as hate, violence, sexual, and self-harm. It operates at the input prompt and output completion level, ensuring generated product descriptions remain compliant with content policies without requiring custom development.

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Prompt Engineering

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Fine-tuning

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Token Limit

Question 11

A company uses Azure OpenAI Service to generate creative product descriptions. They want to increase the randomness and variety of the generated outputs to produce more diverse suggestions. Which parameter should they increase?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output by scaling the logits before applying the softmax function. Increasing temperature (e.g., from 0.7 to 1.0) flattens the probability distribution, making lower-probability tokens more likely to be chosen, which increases diversity and creativity in generated text.

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Top_p

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Frequency penalty

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Presence penalty

Question 12

A company uses Azure OpenAI Service to generate executive summaries of lengthy reports. The generated summaries sometimes include information that was not present in the original report, making them unreliable. Which Azure OpenAI Service feature should the company use to anchor the model to the provided report content?

Accepted Answer

Use the 'Add your data' feature (also known as 'Azure OpenAI on your data'). The 'Add your data' feature (Azure OpenAI on your data) allows the model to ground its responses in the specific content you provide, such as the original report. This prevents the model from generating information not present in the source, addressing the hallucination issue directly by restricting the model's knowledge base to the uploaded documents.

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Increase the temperature parameter

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Increase the frequency_penalty parameter

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Use the system message to instruct the model to only use provided content

Question 13

A developer uses Azure OpenAI Service to generate product descriptions. They want to ensure that the model only considers the most likely tokens that together have a cumulative probability of 0.95, ignoring very low-probability tokens that could lead to nonsensical outputs. Which parameter should they configure?

Accepted Answer

Top_p. (Top_p) is correct because the developer wants to limit token selection to those with a cumulative probability of 0.95, which is exactly what the Top_p (nucleus sampling) parameter controls. By setting Top_p to 0.95, the model will only consider the smallest set of tokens whose combined probability mass reaches 0.95, effectively ignoring low-probability tokens that could produce nonsensical outputs.

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Temperature

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Frequency penalty

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Presence penalty

Question 14

A developer uses Azure OpenAI Service to generate product name suggestions. They want to ensure the model never outputs a specific word, such as 'Corporation', because it is too formal for their brand. Which parameter should the developer configure to reduce the probability of that token being generated?

Accepted Answer

Logit Bias. Logit Bias is the correct parameter because it directly modifies the logits (raw prediction scores) for specific tokens before the softmax function, allowing the developer to reduce the probability of generating a particular token like 'Corporation'. By setting a negative bias value for that token's ID, the model is less likely to output it, even if it would otherwise be a high-probability choice. This is the only parameter that provides token-level control over output content.

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Temperature

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Top P (Nucleus Sampling)

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Frequency Penalty

Question 15

A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?

Accepted Answer

Use Azure OpenAI On Your Data to connect to a product database so the model retrieves and references only the provided specifications.. Azure OpenAI On Your Data allows the developer to ground the model's responses in a specific data source, such as a product database. This ensures the model retrieves and references only the provided specifications, preventing the generation of external or invented information (hallucinations). By using this feature, the model's outputs are strictly based on the connected data, aligning with the requirement for factual accuracy.

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Increase the frequency penalty to discourage the model from repeating common phrases.

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Decrease the temperature to 0 so the model always picks the most likely next token, making it more predictable.

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Enable content filtering to block any outputs that contain harmful or biased language.